在特征筛选时调用to_dict()方法出现问题
```python
import pandas as pd
ct=pd.read_excel(r'培训题1-阿片类药物危机\培训题数据\MCM_NFLIS_Data.xlsx',sheet_name=1)
X=ct['DrugReports']
y=ct['TotalDrugReportsCounty']
# 分割数据,依然采样 25% 用于测试。
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33)
# 类别型特征向量化。
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer()
X_train = vec.fit_transform(X_train.to_dict(orient='records'))
X_test = vec.transform(X_test.to_dict(orient='records'))
# 输出处理后特征向量的维度。
print(len(vec.feature_names_))
报错:
Traceback (most recent call last):
File "C:\Users\ASUS\Desktop\阿片\shaixuan.py", line 11, in <module>
X_train = vec.fit_transform(X_train.to_dict(orient='records'))
TypeError: to_dict() got an unexpected keyword argument 'orient'
尝试过更换pandas版本,但没有用,查看源代码方法里有orient这个参数,不知道怎么办